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Keywords: FAIR  or Deep learning 

WEBINAR: Getting started with deep learning

This record includes training materials associated with the Australian BioCommons webinar  ‘Getting started with deep learning’. This webinar took place on 21 July 2021.

Are you wondering what deep learning is and how it might be useful in your research? This high level overview introduces deep...

Keywords: Deep learning, Neural networks, Machine learning

WEBINAR: Getting started with deep learning https://dresa.org.au/materials/webinar-getting-started-with-deep-learning-986aa2d2-594a-4a7f-836c-44d6e9d5d017 This record includes training materials associated with the Australian BioCommons webinar  ‘Getting started with deep learning’. This webinar took place on 21 July 2021. Are you wondering what deep learning is and how it might be useful in your research? This high level overview introduces deep learning ‘in a nutshell’ and provides tips on which concepts and skills you will need to know to build a deep learning application. The presentation also provides pointers to various resources you can use to get started in deep learning. The webinar is followed by a short Q&A session. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Getting Started with Deep Learning - Slides (PDF): Slides used in the presentation   Materials shared elsewhere: A recording of the webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/I1TmpnZUuiQ Melissa Burke (melissa@biocommons.org.au) Deep learning, Neural networks, Machine learning
WEBINAR: AlphaFold: what's in it for me?

This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.

Event description 

AlphaFold has taken the scientific world by storm with the ability to accurately predict the...

Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning

WEBINAR: AlphaFold: what's in it for me? https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me-4d1ea222-4240-4b68-b9ae-7769ac664ee0 This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023. Event description  AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.  Beyond the hype, what does this mean for structural biology as a field (and as a career)? Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases. Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/4ytn2_AiH8s Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
WORKSHOP: Make your bioinformatics workflows findable and citable

This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023.

Event description

Computational workflows are invaluable resources for research communities. They help...

Keywords: Bioinformatics, Workflows, WorkflowHub, FAIR, Open Science

WORKSHOP: Make your bioinformatics workflows findable and citable https://dresa.org.au/materials/workshop-make-your-bioinformatics-workflows-findable-and-citable-74e85d1c-d869-429e-b942-8391f4bab23d This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023. Event description Computational workflows are invaluable resources for research communities. They help us  standardise common analyses, collaborate with other researchers, and support reproducibility. Bioinformatics workflow developers invest significant time and expertise to create, share, and maintain these resources for the benefit of the wider community and being able to easily find and access workflows is an essential factor in their uptake by the community. Increasingly, the research community is turning to workflow registries to find and access public workflows that can be applied to their research. Workflow registries support workflow findability and citation by providing a central repository and allowing users to search for and discover them easily. This workshop will introduce you to workflow registries and support attendees to register their workflows on the popular workflow registry, WorkflowHub. We’ll kick off the workshop with an introduction to the concepts underlying workflow findability, how it can benefit workflow developers, and how you can make the most of workflow registries to share your computational workflows with the research community. You will then have the opportunity to register your own workflows in WorkflowHub with support from our trainers.  Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. 2023-03-21_Workflows_slides (PDF): A copy of the slides presented during the workshop Materials shared elsewhere: A recording of the first part of this workshop is available on the Australian BioCommons YouTube Channel: https://youtu.be/2kGKxaPuQN8 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, WorkflowHub, FAIR, Open Science
WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia

This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022.

Event description 

Have you discovered a brilliant...

Keywords: Bioinformatics, Workflows, FAIR, Galaxy Australia

WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia https://dresa.org.au/materials/webinar-here-s-one-we-prepared-earlier-re-creating-bioinformatics-methods-and-workflows-with-galaxy-australia-134a8bf5-3801-421f-a454-e0f9020f4871 This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022. Event description  Have you discovered a brilliant bioinformatics workflow but you’re not quite sure how to use it? In this webinar we will introduce the power of Galaxy for construction and (re)use of reproducible workflows, whether building workflows from scratch, recreating them from published descriptions and/or extracting from Galaxy histories. Using an established bioinformatics method, we’ll show you how to: Use the workflows creator in Galaxy Australia  Build a workflow based on a published method Annotate workflows so that you (and others) can understand them  Make workflows finable and citable (important and very easy to do!) Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. GalaxyWorkflows_Slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/IMkl6p7hkho Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, FAIR, Galaxy Australia
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description 

bio.tools provides easy access to essential scientific...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software-aea38c9e-0b40-4308-bafd-f7580563f520 This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. Event description  bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. biotools_EDAM_slides (PDF): A PDF copy of the slides presented during the webinar.   Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Research Data Governance

This video contains key information for those who make research data-related decisions. It will help project leaders to start investigating ways to develop their own data governance policy, roles and responsibilities and procedures with the input of appropriate stakeholders.

If you want to share...

Keywords: data governance, data, research, FAIR, data management, authority, share, reuse, access, provenance, policy, responsibilities, ARDC_AU, training material

Research Data Governance https://dresa.org.au/materials/research-data-governance-6ad9ab90-1a29-41db-b4aa-f1988501530d This video contains key information for those who make research data-related decisions. It will help project leaders to start investigating ways to develop their own data governance policy, roles and responsibilities and procedures with the input of appropriate stakeholders. If you want to share the video please use this: Australian Research Data Commons, 2021. Research Data Governance. [video] Available at: https://youtu.be/K_xVQRdgCIc  DOI: http://doi.org/10.5281/zenodo.5044585 [Accessed dd Month YYYY]. contact@ardc.edu.au Martinez, Paula Andrea (type: ProjectLeader) Wilkinson, Max (type: Editor) Callaghan,Shannon (type: Editor) Savill, Jo (type: Editor) Kang, Kristan (type: Editor) Levett, Kerry (type: Editor) Russell, Keith (type: Editor) Simons, Natasha (type: Editor) data governance, data, research, FAIR, data management, authority, share, reuse, access, provenance, policy, responsibilities, ARDC_AU, training material
ARDC Skills Landscape

The Australian Research Data Commons is driving transformational change in the research data ecosystem, enabling researchers to conduct world class data-intensive research. One interconnected component of this ecosystem is skills development/uplift, which is critical to the Commons and its...

Keywords: skills, data skills, eresearch skills, community, skilled workforce, FAIR, research data management, data stewardship, data governance, data use, data generation, training material

ARDC Skills Landscape https://dresa.org.au/materials/ardc-skills-landscape-56b224ca-9e30-4771-8615-d028c7be86a6 The Australian Research Data Commons is driving transformational change in the research data ecosystem, enabling researchers to conduct world class data-intensive research. One interconnected component of this ecosystem is skills development/uplift, which is critical to the Commons and its purpose of providing Australian researchers with a competitive advantage through data.   In this presentation, Kathryn Unsworth introduces the ARDC Skills Landscape. The Landscape is a first step in developing a national skills framework to enable a coordinated and cohesive approach to skills development across the Australian eResearch sector. It is also a first step towards helping to analyse current approaches in data training to identify: - Siloed skills initiatives, and finding ways to build partnerships and improve collaboration - Skills deficits, and working to address the gaps in data skills - Areas of skills development for investment by skills stakeholders like universities, research organisations, skills and training service providers, ARDC, etc.   contact@ardc.edu.au skills, data skills, eresearch skills, community, skilled workforce, FAIR, research data management, data stewardship, data governance, data use, data generation, training material
ARDC Your first step to FAIR

This workshop gives a brief overview of the FAIR principles, including a method to make a one-file dataset FAIR.

Keywords: training material, FAIR, data, workshop

ARDC Your first step to FAIR https://dresa.org.au/materials/ardc-your-first-step-to-fair-1ee3dc3c-23b0-4287-b96c-c120c5697932 This workshop gives a brief overview of the FAIR principles, including a method to make a one-file dataset FAIR. contact@ardc.edu.au Stokes, Liz (type: Editor) Martinez, Paula Andrea (type: Editor) Russell, Keith (type: Editor) training material, FAIR, data, workshop
ARDC Training Materials Metadata Checklist v1.1

The ARDC Training Materials Metadata Checklist aims to support learning designers, training materials creators, trainers and national training infrastructure providers to capture key information and apply appropriate mechanisms to enable sharing and reuse of their training materials

Keywords: checklist, Training material, FAIR, standard, requirements, metadata

ARDC Training Materials Metadata Checklist v1.1 https://dresa.org.au/materials/ardc-training-materials-metadata-checklist-v1-1 The ARDC Training Materials Metadata Checklist aims to support learning designers, training materials creators, trainers and national training infrastructure providers to capture key information and apply appropriate mechanisms to enable sharing and reuse of their training materials contact@ardc.edu.au checklist, Training material, FAIR, standard, requirements, metadata
Locking the front door without leaving the windows open: positioning authentication technologies within the "Five Safes" framework for effective use of sensitive research data

This project explores the options for access to sensitive data sets; what authentication technologies (e.g. multi-factor authentication) are needed to access sensitive data and secure compute environments.  This project seeks to position choices around authentication technologies within the Five...

Keywords: ARDC, Storage and Compute Summit, FAIR, Infrastructure, NCRIS, eResearch, training material

Locking the front door without leaving the windows open: positioning authentication technologies within the "Five Safes" framework for effective use of sensitive research data https://dresa.org.au/materials/locking-the-front-door-without-leaving-the-windows-open-positioning-authentication-technologies-within-the-five-safes-framework-for-effective-use-of-sensitive-research-data-b83124f8-2add-41c6-b194-d5dd50d098f6 This project explores the options for access to sensitive data sets; what authentication technologies (e.g. multi-factor authentication) are needed to access sensitive data and secure compute environments.  This project seeks to position choices around authentication technologies within the Five Safes framework for research use of sensitive data, proposed in 2003 by Felix Ritchie of the UK Office of National Statistics: • Safe Projects: is the proposed research use of the data appropriate?  • Safe People: can the users be trusted to use the data in an appropriate manner?  • Safe Settings: does the access facility limit unauthorised use? • Safe Data: is there a disclosure risk in the data itself? • Safe Outputs: are the research results non-disclosive i.e. they do not compromise privacy or breach confidentiality? contact@ardc.edu.au ARDC, Storage and Compute Summit, FAIR, Infrastructure, NCRIS, eResearch, training material
ARDC FAIR Data 101 self-guided

FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles

The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.

The course structure was based on 'FAIR Data in the...

Keywords: training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-2d794a84-f0ff-4e11-a39c-fa8ea481e097 FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course. The course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management
Deep Learning for Natural Language Processing

This workshop is designed to be instructor led and consists of two parts.
Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset.
Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for...

Keywords: Deep learning, NLP, Machine learning

Resource type: presentation, tutorial

Deep Learning for Natural Language Processing https://dresa.org.au/materials/deep-learning-for-natural-language-processing This workshop is designed to be instructor led and consists of two parts. Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset. Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for attendees to train their own RNN. The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop introduces natural language as data for deep learning. We discuss various techniques and software packages (e.g. python strings, RegEx, NLTK, Word2Vec) that help us convert, clean, and formalise text data “in the wild” for use in a deep learning model. We then explore the training and testing of a Recurrent Neural Network on the data to complete a real world task. We will be using TensorFlow v2 for this purpose. datascienceplatform@monash.edu Deep learning, NLP, Machine learning
Getting Started with Deep Learning

This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning...

Keywords: Deep learning, Machine learning

Resource type: presentation

Getting Started with Deep Learning https://dresa.org.au/materials/getting-started-with-deep-learning This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning application. The lecture also provides pointers to various resources you could use to gain a stronger foothold in deep learning. This lecture is targeted at researchers who may be complete beginners in machine learning, deep learning, or even with programming, but who would like to get into the space to build AI systems hands-on. datascienceplatform@monash.edu Deep learning, Machine learning
Semi-Supervised Deep Learning

Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled...

Keywords: Deep learning, Machine learning, semi-supervised

Resource type: presentation, tutorial

Semi-Supervised Deep Learning https://dresa.org.au/materials/semi-supervised-deep-learning Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled data. If successful, it allows better training of a model despite the limited amount of labelled data available. This workshop is designed to be instructor led and covers various semi-supervised learning techniques available in the literature. The workshop consists of a lecture introducing at a high level a selection of techniques that are suitable for semi-supervised deep learning. We discuss how these techniques can be implemented and the underlying assumptions they require. The lecture is followed by a hands-on session where attendees implement a semi-supervised learning technique to train a neural network. We observe and discuss the changing performance and behaviour of the network as varying degrees of labelled and unlabelled data is provided to the network during training. datascienceplatform@monash.edu Deep learning, Machine learning, semi-supervised
Introduction to Deep Learning and TensorFlow

This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise.
Part 1 - Introduction to Deep Learning and TensorFlow
Part 2 - Introduction to Convolutional Neural Networks
The Powerpoints contain...

Keywords: Deep learning, convolutional neural network, tensorflow, Machine learning

Resource type: presentation, tutorial

Introduction to Deep Learning and TensorFlow https://dresa.org.au/materials/introduction-to-deep-learning-and-tensorflow This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise. Part 1 - Introduction to Deep Learning and TensorFlow Part 2 - Introduction to Convolutional Neural Networks The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop is an introduction to how deep learning works and how you could create a neural network using TensorFlow v2. We start by learning the basics of deep learning including what a neural network is, how information passes through the network, and how the network learns from data through the automated process of gradient descent. Workshop attendees would build, train and evaluate a neural network using a cloud GPU (Google Colab). In part 2, we look at image data and how we could train a convolution neural network to classify images. Workshop attendees will extend their knowledge from the first part to design, train and evaluate this convolutional neural network. datascienceplatform@monash.edu Deep learning, convolutional neural network, tensorflow, Machine learning